Shaun Newman Podcast - #528 - Regina Watteel

Episode Date: November 8, 2023

She has her PhD in statistics and is the author of Fisman's Fraud: The Rise Of Canadian Hate Science. Gena tells us all about how they used blatant fraud in stats throughout the pandemic and how the government will not acknowledge it.  Let me know what you think. Text me 587-217-8500 Substack:https://open.substack.com/pub/shaunnewmanpodcast

This is Graham Wardle. Mark Friesian. This is Marty up north. This is Alex Kraner. I'm Rupa Supermania. This is Tom Longo, and you're listening to the Sean Newman podcast. Welcome to Sean Newman podcast. Happy Wednesday, folks.

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com. She has her PhD in Statistics, and she's the author of Fisman's Fraud, the Rise of Canadian Hate Science. Talking about Regina, Patil. So buckle up. Here we go. Welcome to the Sean Numa podcast. Today I'm joined by Regina with Teal. So how are you doing, ma'am?

I'm doing great. Thanks, Sean. Well, thanks for coming on. Now, let's start here. People are going to ask, who is Gina? And where is she from? And all these lovely questions. And maybe how she got here, honestly.

And then we can get into your book and all the other things that you're talking about. Okay. I always actually find that a difficult question to ask because I'm not sure where to start. Before the pandemic, my biggest role was being a mom. But prior... That's a pretty big role, I would say.

Yeah. How many kids do you have? I have three kids. I have three kids. So when the pandemic started, my youngest was in grade eight, and I had two in university. They were going through and they were in varsity sports. So they were pretty big athletes, track and field. They were set to go to the nationals, actually, when the pandemic hit.

But then that got canceled. Earlier, like when my kids were quite young, at that time, my career, I was very career focused. So I was a principal statistician for a local economics firm. So my background, I have a bachelor's of math and physics. And I have a master's and PhD in statistics. So when I was working at the, as a principal statistician, I did a lot of risk benefit and options analysis and that type of thing, a lot of statistics.

But then there was a point in my life where I, I got into an accident. A car crushed me between two vehicles when I was putting groceries in my car. What? And yeah, it was not a good time. So after there's a couple years. How does that happen? Okay.

Well, after work one day, we had picked the kids up and we went to the grocery store to pick up some things for supper. And the kids were in the car and my husband was in the car. and I ran into the store, picked up some food, was putting it in the trunk, and another vehicle came in and smashed me between the two vehicles. So this was a drug dealer, actually. He was noticeably high, and he hadn't had a license since the 80s. Like, it was bad. And the weather wasn't very good, right?

It was like he didn't clear his windshield. It was like February, early February. In the series of bad luck or whatever, that's, like, that's tough. Well, it is tough. I don't think there's a real appreciation at the time, like how bad it is, because your legs are crushed and they've blown out really big. You know, it's a lot of, you know, vascular kind of damage. And you can't walk and you can't stand.

It's very, very painful. But you think, oh, yes, I'll get through this. I'll be better like in a week, two weeks, you know, months go on. It's, it was very challenging. And then that was followed by a series of other unfortunate events. So it was like two years of you just can't believe what we went through. So my husband was doing so much. He had to do pretty much everything for a time.

And then in his family, there was a series of, of deaths so like you know four there was four over that two years span uh ending with his his older brother um and then during that time he also found out that our neighbor was a convicted pedophile so it was just an unbelievable time um so did you have to work were your kids around the neighbor i mean obviously he's your neighbor no well the thing is that uh Well, I had, I always kept a very, very close eye on my kids. So they were never really outside. They were quite young.

So they were always with me. So it wasn't an issue. But of course, when we found out, it's like, you know, you don't want to be in a neighborhood where that's happening. And so we actually just picked up and moved to a country setting. So now we're like on a hobby farm. We don't really have neighbors. We have like cornfields. So it was, and it was a result of that in the accident.

And a lot of things happening. we just wanted to kind of get away. What a, what a, yeah, that's a, well, I got three young kids, right? Seven, six, four. And so I think on the list of things you're like, uh, crap, right? Like, that ain't good. A pedophile next door would be probably on the top of that list, I would say, pretty, pretty close. Yeah.

Yeah. Yeah, it was, it was two, like following the accident, it was two years, it was very, very, very, very hard and we tried to keep things together as much as possible. And, you know, I was still working during that time. But it was really too much. It was really too much to keep everything together. And then in my late 30s, I just stepped down from my career and decided to focus on more on health, rehab, the kids.

So we did a lot of focusing on their sports, music, education, that type of thing. And then for my husband, he's a really good, he's very creative. He's a good writer. So we decided to do fantasy novels with him. Really? So that's, yeah. So that's kind of where it was when the pandemic hit. Everything was kind of coming together.

So he had written some books that were close to being published. And my children were doing great in sports. Like I said, my older two were varsity athletes and track of peace. They were set to go to the nationals. My youngest enjoyed hockey and softball and that type of thing. And then the pandemic hit and everything was cancelled. So that took us on yet another journey. So with myself, when they announced the pandemic

and they announced the two weeks to flatten the curve, I was very, I couldn't believe it actually. I couldn't believe it actually. I went on my computer right away. I got as much of the worldwide data as there was and did my own risk assessment of the situation. So this kind of comes naturally to me when I used to do it all the time at work, like risk risk analysis, that type of thing.

So when I looked at it, and this was like March 2020, so it's right away and I looked at it. And right away, I was relieved, actually. I looked and I thought, oh, good. The kids will be fine. The kids will be fine. We're at fairly, you know, low risk. The kids were at almost none.

Myself and my husband, you know, we're healthy people. You can see right away this mostly affected very old and those with comorbid conditions. So my focus just shifted to like my parents. So I gave them a call and, you know, totally. them to be careful and that my siblings would bring them their groceries, that type of thing. And then, of course, I was, you know, more concerned. I had two siblings in health care, so I focused on them because they worked in the hospitals. But overall, I wasn't too worried

about what I was seeing in terms of the pandemic. And then when I was looking at what the government was doing and the lockdowns, I was quite surprised because that's not how you would mitigate your risk. You did not need to do what they were doing. They seem to be acting quite contrary to what the data was saying. So initially you think, okay, they'll calm down and, you know, as they learn more, they will, you know, act accordingly. But they didn't. It just kind of seemed to get worse and worse. And this was very unnerving for me. You know, you sit in a very interesting position with your background and everything. Because how early are you looking at the pandemic doing your risk assessment and realizing,

well, I mean, the kids are going to be fine. The only people that we really got to worry about are the elderly and maybe the health care workers. And you're like, okay, and moving on from this. Like, is that in the first three months, the first year? the first you know is when do you do this risk assessment that you realize all this no i i did it i did it right away so basically driving to starbucks to get to get to get my latte and it was announced uh premier for i'm in ontario so premier ford announced that he would do a two-week lockdown following the march break um because of the pandemic and at that that's

point I'm like what it wasn't it really wasn't on my my radar and I knew a lot of families who had you know just picked up their kids and went to Florida for the March break and we're all like yeah it's March break so at that time I came home fired up my laptop and started you know downloading the data right away it took a little bit just to get my my spreadsheets all up and everything but the analysis was like easy it was it was it's ought to be one of the easiest analysis I've ever done because the, we had a lot of early information and we could see what was going on and you could see right away that it was mostly

impacting the elderly. And by, and the difference between the young and the old, you know, in terms of risk, changed a thousandfold. And so I'm thinking, wow, this, in terms of, you know, a virus or a disease, you know, you. You don't want to say, it actually was good news, in my opinion, for the young and the economy, because it really did not have to affect working age people and the young. So it should not have had the impact it did on our economy. Right away, we should have put resources towards making sure that the elderly and those with comorbid conditions, were safe. That's where the emphasis should have went. Gina, I somehow I missed it. How quickly did

you, how far into the pandemic did you do this analysis? So it's March 2020. It was, oh my God. It was right away in 2020. It was, it was soon as I looked at the data. It was, you know, you know, you know, you know, you know, you're not, you know, I think there's a whole chunk of us that understand that there's a nefarious plan and and we saw it unfold through the years and everything else. But, you know, I've heard, I've had different people on here who have been like, you know, I just knew right away and this is why and this and blah, blah, blah. But here it is a statistician who's done the risk analysis going in March of 2020. I can tell already that it's the young folks are going to be fine. But like it literally, well, I mean, right now in Canada,

They're talking that, you know, get your booster, get your, your updated COVID vaccine, and as young as six months. Like, why haven't you gone and done that? And you're like, this is the, and here you are, Gina, saying, well, March 2020, I could tell them, you know, and one of the, one of the things that I struggled with all through COVID, where were the voices like Gina, right? And like, nowhere in, like, it was like, everybody just lost their bloody minds all at the same time. And nobody was willing to have a bit of a conversation to, like, de-escalate what was going on.

Well, so first I looked at the pandemic, like, you know, just because I'm worried for my parents, worried from my siblings. So I was taking it very, very seriously. But, I mean, seriously, but I was just not worried from my household at all, at all. Until I think it was around the 23rd, 24th of March. It was around there where the federal. government stepped up and basically wanted to give themselves unlimited power for like a couple years because of this pandemic. And that was like within a couple weeks of calling the pandemic. So right away they were trying to grab a bunch of power. So when I seen that, I'm like, oh my God,

this I it did it did not feel right at all. And then to your point, why weren't people standing up? I was thinking the same thing. I was thinking, this is pretty obvious. It's obvious. You don't need a PhD in statistics to see it. So I figured there were many scientists and many statisticians and economists, many people must see that we're going down the wrong path. But why aren't they saying anything?

And so I did a lot of research April, May, looking for those voices. And that's when I started seeing more and more censorship of scientists, actually, in scientific papers. So that's when I realized there was a lot of censorship going on. And then you know there's something, like we're in really big trouble. So, yeah. It's almost when you look back at the last three years, you know, you think we're out of it and we're on to better days and everything else. but when I listen to, you know,

the censorship thing is very, very prevalent in our society today. You know, the, I mean, it just, the coup's four. I probably don't need to bring this up. Well, actually, we probably should be bringing it up more. But they've been in remand now for 600 and, I don't know, it's closing in on 700 days. And they had an envelope that was supposed to get unveiled, and they've sealed it and they're not going to talk about it and they can't use it as evidence supposedly and and you go and that's happening right and you got you know you got Jeremy McKenzie with the you know gets all these charges placed against them and then you find out hmm it's coming out more and more that that wasn't on the up and up and so that that but nobody's talking about it the the censorship of even when they get things drastically wrong kind of just get sort of

swept under the rug. And that is Canada today. Yeah, the censorship is absolutely necessary for them to do what they're doing. It's absolutely necessary for them to have that kind of control. And one of the main reasons is because their evidence is so weak. It is so bad. You can you can poke so many holes in it. There's no way it can withstand scrutiny at all. Especially when you look at the statistics that they're putting out, I mean, what they're doing fails, basic statistics. Like statistics 101, they would fail first year course. It's so bad. It's so easy to dismantle what they are doing.

So they have to, they have to censor real scientists, really. Like, I don't know how you can be a good statistician and go along with this. I don't. Well, let's think about that for a second. Because I sit here, I took stats in college, and that was a tough course. So my hat's off too. I did find it interesting, but it was a tough course. It's not like you're, and I don't mean this in the wrong way, it's not like you're unique.

There's tons of people that are statisticians. Is it this giant number? Is it like a petroleum engineer out in Alberta? Maybe not. But it's not like it's, you know, in the realm of Connor McDavid in the NHL. How many of those are there? I don't know, five maybe players in that range. Like there's got to be a whole grouping of statisticians across all of Canada, across the bloody world.

And they, like, if they looked at it, because they probably do, because the one thing I love about your story is, you know, well, I created a couple spreadsheets and rattle off my data. And within a couple minutes, I'm like, well, this doesn't hold water. It's like they had to have done the same thing. They would have been compelled to do the same thing because you love playing with numbers and seeing what happens, I assume. Well, I mean, I'll give it to people that maybe they,

if you don't look at it, then yeah, you can go along with what's happening. And if you don't look at it, you don't want to look at it, and you trust the experts that the government has chosen for you to listen to, If you have that kind of trust and that's good enough for you, maybe not everybody has a curious mind. I don't know. But if you do have the background and you did look at it, I don't see how you could have gone along with it. And like I said, you don't have to be a sad decision to see it. Like my household, we're very into the sciences here.

Both myself and my husband were into, we met in the math physics program when we were younger. My oldest son, he just got his degree in applied math and physics. So we're very mathematical. My daughter isn't as much interested in mathematics. She's more in social sciences. But she was the first one in the household to read the, clinical trials for the vaccines. And she came to me and said,

Mom, what is this? It's terrible. And she started going through all the things wrong with it. Your daughter did? Yeah, and she's the one person in the family who wasn't into science. So I'm thinking you don't need to be a scientist to see it. First off, hats off to you. That's super cool.

You think of grown adults don't want to read the papers on it, let alone, you know, other statisticians or science folk, right? No, they're just trust blatant. No, it's all good. How old was your daughter while she was doing that? Well, she was in university. So university. Yep.

Yeah, so she was in university. She was the varsity. Still, hats off to you, Gina. That's pretty cool. Well, the thing was that when the pandemic hit, it hit the kids really hard because they were very, very social. They were going out all the time to their track meets. with their friends and track and field was a very big part of their life. And that got taken away completely.

So right away, they're waiting like, oh, when can we get back to it? So we were looking at what the government was doing. And I would share with them. We were very open on the breakfast table, for example. I'd say, oh, look what they're saying now. And the kids would sit there and critique just logically how flawed it was. And they'd kind of, you know, laugh about it because they figured it would pass and it was so ridiculous, but it didn't pass. And so once it became obvious that, like to my daughter,

especially when she was told that, yeah, your track and field is over, it's not going to happen. You know, it was devastating. She was crying. It was awful. So my son, he's two years younger than her, like her, my oldest son. And he kept thinking that he was going to go back to varsity sports and in 2021 when it looked like they were going to have a you know they were going to open it up he was he was thrilled but then right away they they said that he'd have to get vaccinated in order to do his sports so that became an issue so because we were keeping an eye on everything when they were talking about the vaccines um i was looking into it right away i was looking into it you know in the fall of 2020 and then when the clinical trial reports were available, we read them to

see like, okay, how is this? And there was a lot of red flags there, a lot of red flags. And there was really no reason for most people to take this because COVID just wasn't much of a risk to most people. Sure, if you're over 70 and you have comorbid conditions, then, then, you know, maybe you'd consider it. But for, for children, there was definitely no reason. There's little to no benefit, even if they worked, even if they had worked perfectly, the benefit just wasn't there for kids because they weren't really at risk of serious illness or death or anything like that. And the risks were very, very, very high. And when I say the risk were high, I have to mention how I look at risk. I look at risk also as a measure of the uncertainty with these new vaccines. So it's not just a matter

of the likelihood of a specific adverse event. It's how much uncertainty do they know about these vaccines? And there's a lot of uncertainty. And I look at that. that and like, well, where there's uncertainty, there is risk. And then when you look at how they are tracking, are not tracking the adverse events, that's even worse. So that's another topic. I try to stay away from talking too much about that because there's a lot of nuances with that. And then there's a lot of debate. And so one step at a time, I look at transmission because to me, that one is really super easy to talk about. And super easy to demonstrate the fraud that went into that.

And then once you dismantled that, you can move on to how they're discussing adverse events. But one step at a time. You were saying that transmission is easy to track. You said fraud, well, I combined it, fraud of transmission. easy to point out that. Explain that for me. Okay, so first of all, when you look at the statistics, we can get into the, oh, I'm not even sure where to start.

I'm not sure we should start with the book, or let me start with the case of Fisman's fraud, which I wrote the book on. And here, I tell you what, I'll bring it up on this. screen this is this is this is the book that when when is it being released Gina so it it will be for it'll be released on Tuesday on the 7th of November okay so by the time this releases it will have been released so people can go on Amazon I assume yeah they can go on Amazon and Fismans fraud and there's the there's the image of it folks so if you want to go pick up a copy that's that's

the way to do it Okay, I'll just say one thing. Oh, can I go back? Oh, yeah, absolutely, yep. Okay, so you see the main book is Fismund's Fraud, The Rise of Canadian Hate Science. And then there is a supplementary guide, Fisman's Fraud, the Accomplices. I just want to make a note that to be as transparent as I could,

I did release the letters that I sent to the different associations that were involved in this. So Fismans brought the accomplices. It's just a supplementary text with letters and emails and that type of thing for those who'd want to see it. But the main book is Fismans Broad, The Rise of Canadian Hate Science. So you're saying you can buy the accomplices as well, and all that is is a combined text of emails, or is that just something that can be found? That's just like the letters, the emails that I sent to Canadian Medical Association Journal. University of Toronto. Yes.

Sorry, Gina. I'm just meaning if I search, where can I find that? Where can I find the accomplices? Oh, they're both. They're both on Amazon. Oh, okay. So if you go to Amazon and you type in,

once again, I'll bring it back up, Fisman's fraud, there's a chance or probability you will see both books. And if you're confused, now that makes sense. One is the letters and everything, and that's the accomplices

and the second or the, the main book is the rise of the Canadian hate science. Right. So most people, if you are interested in this story, you'll want the rise of Canadian hate science. It's just for people who maybe want proof or that I sent things. That are very thorough and want to see it. They'll want both, essentially. Right.

Yes. I appreciate you explaining that because I guarantee there will be people who buy both because they'll be like, well, I got to see what she sent because it's, it's, solidifies it even more. Okay. Yeah. So with Fisman, what he and his two colleagues did was they basically used mathematical models to scapegoat the unvaccinated Canadians.

They cast unvaccinated Canadians as disease carriers. Before you get to that, I apologize to interjecting Fismet. Who is he? Okay. So David Fisman is a tenured professor at the University of Toronto. He also served on the Ontario Science table and whatnot. So the book does go through his connections and who he is. It's on one of the slides if you wanted to bring that up.

Sure. Slide number six. Slide number six. Right there. Okay. So with Fisman, he was on the Ontario COVID-19 science advisory table. And that was an initiative of the Dallelana School of Public Health out of University of Toronto. And they advised the Ontario government during the pandemic.

And they also, you know, advised public health agency of Canada, health Canada, in the government of Ontario in general. Fisman, when he was doing this study, he was tethered to a lot of pharmaceutical companies, Moderna, Pfizer, AstraZeneca, you know, and so forth. He also did some legal roles for the, for ETFO, the Eleventure Teachers Federation of Ontario, the Registered Nurses Association.

So basically he advised these organizations with respect to COVID-19. So for example, ETFO, the schools, they were looking into, you know, the school closure. So he was all for closing the schools and that type of thing. The nurses association, they wanted mandates, vaccine mandates. So he was a proponent of that. When you have him tied, like when I look at Pfizer, it goes directly to him. What was it about him and Pfizer that were connected? So if you go through my book, it just talks about how he's on the advisory table

for different pharmaceutical companies. So he has affiliations with these. these pharmaceutical companies in advisory type of positions. And that just creates a conflict of interest when you are writing a paper that basically is promoting or advocating for vaccine mandates. So you have a conflict of interest there. And so he does, this is reported in the paper. It's not hidden.

This little diagram you can get just by looking at, If you go to the Ontario COVID-19 science advisory table, they have to list their potential conflicts of interest. So you can get most of these just looking at that. So these associations are well known. It doesn't take a lot of digging to see them. Well, a lot of this doesn't take a whole lot of digging to see it. But that's what is kind of head scratching at times. You know, because if you, what did they say, stop doing your own research pretty much.

you know and and that was always an interesting way to to look at it from uh well i don't know government standpoint well the thing is that they it's funny because you there's a post to um to make known their conflicts of interest but just because you you report that you have these affiliations doesn't make the conflict of interest go away you know what i mean yes i mean there's a conflict of interest, look into it and, and, you know, and maybe it's a conflict of interest and we should just stop and find somebody who doesn't have 50 million conflict of interest. Yeah, but the issue also is if you have a conflict of interest and then you go through the paper and you see a whole lot of problems, that's also a red flag. It's like, hey, is these conflicts

of interests, are they playing a role? So if everything was done on the up and up and it was legitimate and you can withstand the scrutiny, then it may be okay. But when you can't, then it becomes an issue. It's like, well, how is this getting passed? And you start to question some of these relationships. Well, go back then. I interjected and took us off on a different thought process, but you were talking about FISMIN and his team and how they were scapegoating the unvaccinated.

Leave me back through what you were trying to address. explain Gina when I interjected. I just wanted to get a better feel for Fisman at the at the beginning. Okay, so why don't we go to, the easiest way to explain Fisman Sprott is to look at what was happening in December of 2021. So maybe if you go to slide number two. Okay, so these are just basically print screens of what was happening in December of 2021. And You have here with about 80% of the population vaccinated and the vaccine passports in full force, you had a huge surge in COVID-19 cases.

So you see the little diagram here, it points to the Omicron wave. And this is basically the first true test of the vaccine's effectiveness, because this is the first winter season where we need protection. And at this point in time, unvaccinated people were not permitted in most public venues. You know, we were banned from restaurants, theaters, a lot of sports.

You've had a lot of employers had basically purged their premises from unvaccinated workers. We couldn't travel by plane or train. And yet with all these restrictions on, we see a huge surge in COVID. COVID-19 cases. So this does not look good already for the vaccines. And then the second graph there is the hospitalizations. So you see even with this milder variant, the hospitalizations like double. And so this is bad news for the vaccines intuitively. Wouldn't you agree, Sean? Yeah, well, you would think you would think if you got 80% of the population vaccinated and you've eliminated those dirty unvaccinated from being able to participate in society that you'd have

relatively zero cases, you know, because you've vaccinated and you've eliminated a lot of contact with, well, the dirty unvaccinated. Right. So that's where things stood. So if you look at the next slide, which is a little bit more of a breakdown. So that's, yeah, here you go. So when you you want to look at the next slide, which is government of Ontario data. So if you look at the left screen, this is just a screenshot. I did not have to do anything except, you know, print screen. And that's what you got from the Ontario government. And it shows the number of cases, COVID cases over that time period in the vaccinated group versus the unvaccinated group. So you can see that the whole surge, like the majority of the surge was was.

vaccinated was was vaccinated by the their own data um so in this even though this was the was the case the rhetoric against the unvaccinated at the time went way up so they were being the small group of unvaccinated cases were being blamed for this surge amongst the vaccinated people and there was talk at this time well not just talk the good government actually imposed more restrictions. So the cross-border restrictions on unvaccinated truckers, for example, more restrictions came in, and then we had the Freedom Convoy come to Ottawa during this time.

So that's what was going on. And it was pretty clear that the vaccines did not curtail transmission. Provinces started to relent on their vaccine passes. and it looked like the federal government was losing the narrative, basically. So that was, you know, winter months of, of, of, I guess, 22 then. So Christmas, 2021, you're into early 2022. And then Fisman study basically comes to the rescue. So if you look on the right hand side there, this is.

what Fismans study their graph. So in this simulated version, you have the unvaccinated was basically responsible for, you know, the majority of the COVID cases and the vaccinated to a lesser degree. Can I feel like, you remember me saying before we started, I'm going to ask some really dumb questions. Here's a dumb question. Can you explain this slide to me? What is 1E plus 05? five even mean. At least on the left hand slide, left side of the page, I'm like, I read that and I go, oh yeah, that makes sense. It's like, you know, for all of us that have ever built a graph or a chart, you know, like that makes sense. The Fismans data, I look at it and I go, what does zero E plus zero zero even mean? Like, I don't understand it. Yeah, so times 10 to the exponent five is, right?

So you'd have your five zeros. So that's another thing with Fismans curve. And I mentioned this, too, in one of the letters, in one of the, in the supplementary text, that he's generally off by an order of magnitude, you know, like his scale is off. But even with the scale being off, and then he also flips the results between what we really saw in reality versus what he says. So what you're saying is, Gina, this is one evil human being. And I know you're not, you, I guess that's just what I hear. because I'm like, why, why, oh, why would you do that? Okay, well, we'll get into, I don't want to, all I'm saying is, is that when I look at the reality and then look at what he's saying, it's like, yeah, he's inverted it.

He's inverted it. Like, I mean, you don't have to, there's, he's inverted it. To me, it's very self-explanatory. It's pretty self-explanatory. Now, some people may come out and say, oh, but there was more vaccinated people in the, population and that's why you have more vaccinated cases and when you look at the case rates maybe things look differently so if you go to the next slide oh i'm trying to change the slide so this is the the rate so this is the you know proportion of people in the vaccinated group

with COVID during this time and then the proportion in the unvaccinated group so again this is the Ontario data just a print screen so I didn't not have to make, I mean, I put the labels on, but I did not have to build anything. I didn't have to build anything. I just say, yep, this is what the Ontario data says. And when you look at this, to be fair, you can look at the Ontario statistics and you can discuss issues with those statistics. And I have. I mean, especially when you look at, at the rates, when you we all know that the way they count is a little bit funny so for example if you got vaccinated and then you you tested positive for COVID within two weeks of that vaccine yes you're listed

unvaccinated you're listed on vaccinated so that so that the so that would increase the estimate for the unvaccinated group to make them look like you know they had a higher rate So that's one thing. Another is that they have a pretty good handle on how many people are vaccinated, but they tend to undercount the number of unvaccinated people in the population. And again, when you do that, the unvaccinated rates will be inflated here. So what I'm saying is this is their graph, but in reality it looks even worse, is likely to be even worse for the vaccines.

Does that make sense? It does. Yeah. And once. Basically. Well, and I was just going to say, and once again, when you look on Fisman's side of the,

right, it makes it look horrific. Yeah, horrific. Look at that, that huge spike in the unvaccinated. Like,

you could imagine being, you can imagine being like a politician or just a business owner and seeing that and going, or just the average population. And seeing that and going, Holy crap, man. Everybody needs to get vaccinated, right?

I mean, it would be very convincing if you didn't know what the heck you were looking at or didn't dig a little deeper. Right, but the problem I have with that is the government of Ontario data is right there for everybody, like the politicians should be looking at their own data. I mean, it's right there. And they had it for months.

For months they had that data. And they wait until Fisman's fake study comes out and then present the fake study in Parliament to justify extending the restrictions. And that's what happened. You had the Liberal MP come out, the Parliamentary Secretary to the Minister of Health. He comes out and says, hey, this study says that, you know, these restrictions and the mandates and everything else is justified. And they run with it. But it's funny because Phisman's study came out in April 25th, 2022.

And by that time, they were collecting data. So April 5th, 2022, sorry, like after the trucker convoy, everything. April 25th, 2022 is when the study came out. So yeah, so this is past the trucker convoy. The provinces had gotten rid of their restrictions. But the federal government wanted to keep its travel restrictions against the unvaccinated and they did not want to do away with the federal vaccine mandates either. They wanted to say, nope, we still need them.

So FISM study came out and they said, hey, look, we still need these restrictions. At the time, though, they had months and months to look at the real data set to say that's not the case. And they even had, at that time, they were attracting the boosted population too. And the problem is that by spring, it was the boosted individuals that had a much, you know, much higher incidence rate than everybody else. So that looks really bad for the vaccines. But the point of it is that basically what Fisman and his colleagues ended up doing was overriding the Omicron wave with a fake simulation that basically said the opposite.

And then based on that, unvaccinated people were scapegoated. And the establishment went along with it. The Canadian Medical Association Journal, they published the study. The federal government funded the study through CihR. many, many scientists wrote to the journal and openly trashed it, basically saying that there was so many flaws in this model and that it's in and that it does scapego, the unvaccinated. So they were apprised of this by many scientists.

It wasn't just me. I think I was a little bit more persistent in my complaints. So what I wanted to do is I wanted to, well, first of all, I wanted the record to be set straight. And I was hoping that they would retract the paper, set the record straight, and investigate the researchers. That's what I was looking for. but what I found was the establishments were more than willing to rubber stamp the fraud. So what you're saying is you were persistent in reaching out over and over and over again to try and get them to acknowledge that there's been fraud essentially and to acknowledge it and have it removed.

And they have basically said, no dice, we are an agreement with what has been written. More or less, yes. So when I look at, when I say fraud, so in science, we're looking at research fraud. And so with research fraud, there's fabrication and falsification. So fabrication would be, you know, making up the results, fabricating results or making up the data results and trying to pass it off as a true reflection of reality. Right. So that also includes making up data when it should be when it should be when you should actually use an experiment or observational data. So there's no real reason for David Fisman to make up data when we have the data readily available.

Okay. So making up data results and then trying to pass it off as a true reflection of events, that's fabrication. Falsification is basically when you manipulate the research process. to get a desired result. All right? So he also did that because basically he decided to do a simulation that would show the unvaccinated are responsible for the COVID surge and that they're a disproportionate risk. And when I say that it was contrived, I mean, it was contrived for that purpose. This was a deterministic model.

you feed in what you want and it spits it out in graphical form. So basically if you just put into the model, okay, we're going to give vaccinated people this high, you know, immunity rate and unvaccinated people, a low one, then you're going to spit out the graph that he spits out. And that's basically what he did. So based on that, you know, one parameter or the difference in that one parameter, you get the result you want. So it's basically there just to get the results that you want. I've always thought

did sales in the oil field for close to a decade. And when you build charts and graphs and everything else, you can kind of creatively manipulated if you want to show an outcome that maybe isn't exactly happening.

You're just changing the the parameters of even just the chart, right? Like, we've seen that when it comes to, oh, I'm spacing on the, me and twos have talked about this on the mashup folks multiple times when they put on a chart and they shrink the left-hand column of what you go up in, you know, it's the first 10,000, and then maybe to shrink it instead of going up to 500,000, you do this like real short jump from there. And then it just changes what the, the graph.

graph shows. And to the average person, it just takes a quick glance at that, you don't think much of it. But if you sit there and stare at it, you go, well, why would they do that? Well, that's manipulating the graph is what you've done. And what's always unnerving when, you know, when I have somebody back on to talk about COVID and everything that came down with that is the level of all of that that has happened to the Canadian population and probably the world, but certainly here in Canada, we all saw it firsthand. And you're just explaining it in way better terms than I ever could about how bad it really was and is.

Right. Well, I'll just finish off with... So it wasn't just they manipulated the graph. They just created a graph that gave them the results they wanted. Right? So it's just outright... Yeah, it's on the... level of bad, it's really bad.

It's really bad. And part of falsification is omitting, omitting observations that go against your desired result, right? But the thing is that they had omit the entire real-world data set in order to get their result. So they did totally omit data that would contradict the narrative that they wanted. So. I'm glad we can, I'm glad we can. laugh about this, you know? It's so bad that the only thing you can do is laugh a little, you know,

because like, it's just like, what do, I mean, this is, this is insanity. And it is, yeah, it's bad. It's, the funny, it's not funny, but when you go into, to academia, they do their, um, their research and academics will look at the research, a lot of them, uh, and I, I do this, well, I'm not in academics anymore, but you, you tend to look at it. from a scientific perspective and go through their research and critique what is wrong with it. And so I did do that. You critique the model, you critique how they got their estimates, you do all that and you show that it has no scientific merit. But sometimes you miss the main point. And the main point with Fismans isn't just that his model was bad. It wasn't just that it had no scientific merit.

It's that it was textbook scientific fraud. Falsification, fabrication, this is considered scientific fraud. So because of that, I felt this was a very good case to highlight because it usually isn't this easy to show fraud. Usually scientists are more careful or not, I'm not saying a lot of them do it, but when there is scientific fraud, there's usually some kind of plausible deniability, but I just don't see it here with this case. So what did politicians, what did people say when you pointed this out and sent them these letters and was like, this is like so blatantly obvious, we need to do something about this? Yeah, if you want to go to, I'll just quickly show maybe slide seven to show the different.

Yeah, so here you go. So I did things in a very methodological way. So the first place I went was to CMAAJ since they published the study. That's Canadian Medical Association Journal. That's where they were flooded with E-letters in the first week. I think there was over 22 other scientists that. sent e-letters saying, hey, there's something wrong with this. You need to retract it or you need to correct it.

And so they were flooded with people, the scientists basically asking for corrections right away within the first week. And I said something to them as well. I sent a critique to their portal asking for a retraction of the paper. But I went further and I sent a critique. them a letter that gave like a 20-page breakdown of all the things bad with this model and why they should totally retract the paper. They basically didn't give me a response. They did not engage in any kind of real dialogue. It was like an automated, we got your thing, you know, we got your

letter. That's about it. So then I went and filed a complaint with the University of Toronto. And I gave them the same document that went through the whole model and all the things wrong with the model explained how bad it was, but also the damage that it was doing, the scapegoating, basically you have a moral obligation and a professional obligation to investigate. And so I went to their integrity office three times. So the first time I sent the letter, they came back, and they denied that their framework dealt with this kind of misconduct. So then I had to go through the framework

and point out exactly the sections in their framework where it deals with fabrication and falsification. So it's like, yeah, you are, you know, you are supposed to handle this. This is what it says in your guides. Then they come back and they say, oh, that's not what's happening. So they tried to deflect it.

They gave me a straw man argument as to why, you know, basically they started to refute things that I was not saying. So they kind of misrepresented my complaint. So then I sent him another letter spelling it out so that there's no misinterpretation. There's no way to wiggle out of it. I really like that letter. That's my favorite letter, actually. My last letter to University of Toronto.

And they responded by saying, case closed. They basically ran away. There was no way of really wiggling out of it, and they just closed the file. So once they close the file, I now can go to the Canadian Institutes of Health Research, CIHR, because they funded the research. And the University of Toronto, when they're given, because the researchers were given CIFR grant money, that if there's something wrong, some kind of complaint, then it's up to the University of Toronto. Toronto to investigate it. And if they don't, then I can go to C.HR and complain about University

of Toronto. So that's what I did. So CihR, I understand it to be that CIFR, because it's, you know, they're, they're answerable to the Parliament of Canada. And because this study was waived around in Parliament, I felt they had a duty to set the record straight because it was used in parliament. And so the presidents got together. It's what the president of CihHR and CERC and I think SSHRC is that how it is. I have to look that up. They got together and they basically said, nope, they're not going to do anything.

Gave it the green light. File closed. So that was the process that I went through. you know, you do a little bit more digging. And I felt that there was more going on here. When the journal didn't respond, I looked a little bit more at what was going on with the journal. And, you know, I was able to look at the preprint of Fisman's paper

and then the changes that were made as it went through the peer review process. And that was very disturbing. because I found that a lot of the fraudulent statements appear to have been made during the peer review process. So the peer review process is supposed to like get rid of problematic statements. They're not supposed to add to them. And at that, and so once I went through this whole process, I decided I would go to the OPP, the Ontario Provincial Police and ask them to investigate. So that was the process I went through. You know, I don't know.

Maybe as soon as you said the University of Toronto said that's not within our guidelines. And, you know, the, you know, if I'm sitting as the U of T, okay, and you come to me, the first thing, okay, but maybe is, well, what are our guidelines? Is that, like, to say that they aren't in our guidelines. And then to have you go check and be like, no, actually they're right there. Like, that's your guidelines right there. is like, well, that's about as enerving as it gets, I think, right? Because to have one person create a terrible study that is bad, bad, bad, on the level of bad, it is bad.

But then to have the institution that employs him go, oh, yeah, we don't need to worry about that. That's terrifying. Like, to me, that means the institution is, he's either complicit, or is literally driving down with nobody at the helm? Well, they try to say that, oh, this isn't really fraud. This is a difference of scientific opinion. You'll see that a lot.

And I've had to go through a lot of, you know, articulation with the university because they try to wiggle it out and use words in funny ways. But I think one of the things that that's really important that maybe I have to mention is there was an attempt. Like they tried to come back and say, oh, this was a modeling study. So modeling, so it's okay. So you have to go back and say several things. First of all, all of science is based on models.

You can't do any statistics without model assumptions at all. I mean, if you don't have a model, you don't have, you can't make inferences. So to say it's, you know, it's modeling. It's like, well, we use models for everything in science. Second, you are using your models to affect real people. So it better be tethered to reality. You can't just make up baloney and then, you know, impose these restrictions on people. But more importantly, there was an attempt to make this look like it was based on real people.

There was that attempt. And if you go through the preprint of this paper that, you know, that was given to C.M. MAJ before publication and you look at it, it's amazing the changes that were made between the pre-print and the final publication. And one of the things is in the pre-print, there was some formulas, differential equations. It was more obviously a model. But as you went to the published version, those were stripped out of the paper. And in the pre-print, they mentioned the word people one time. But by the time it was published, it was meant people were mentioned 76 times. People this, people, people, people, people. Seventy-six times was mentioned in that article.

And there were no people in this study. None. This was, there was no people. So when you say you found that, you know, people, you know, who were unvaccinated were, had a disproportionately higher risk, you did not, they did not find that people anything. There was no people in the study. So. Yes, this is, you know, it doesn't seem to matter how far away we get from 2020 specifically. When I have a guest come on and lay it down so concisely,

you just realize what we're up against, you know? Like, this is, this is, um, this is the machine, you know? Like, it's, it just, it, it wanted a certain outcome and it was willing and still is willing to protect it at all costs. That's all I see. Yes. And I think people have to understand that is the case and start questioning why. So you do a lot more digging to try to figure out why because a lot of people like to believe that this is behind us. But there's, why would a reputable university go along with this? Why would CihHR not correct the record?

Why? And the answers are not nice, but they're right in front of us. So when you look at what David Fisman says about his study and what led him to do the study, they've been pretty open. as to the motivations behind the study. And it's even in his paper where he writes that the results of the study undermine the assertion

that vaccine choice is best left to the individual. This is key. When you look at the reason behind the study, they want to demonstrate that it shouldn't be left to the individual, that vaccine choice should not be an individual right. So you see that there is an attempt to perhaps reinterpret or look at section 7 of the charter in a different way in which we should not be looking at vaccine choice as an individual right. That is where I believe the main reason for the study was for that.

And you see that that that is gaining traction, that whole notion that, oh, you need to vaccinate to protect me has really gained a lot of traction in the last couple of years. Even though it's completely bogus. It's completely bogus. And I think you have to realize that the charter is supposed to be there to protect individual rights. And here we have a case where researchers were willing to commit. fraud and the you know the academic institutions were willing to back it the the government was willing to fund it all right we need protections we need protections in place when this happens so this is my book kind of

goes through that whole arc and it talks about the journey it also talks about the um all the safeguard failures that occurred are the main safe safety safety that occurred over the past a couple of years to bring us to this point because we didn't just get to this study. There was a whole bunch of safeguard failures along the way. So it lays that out as well. Is there anything else, Gina, you've laid down a ton this morning. You know, I always talk about Chris Sims because when she comes on the show, Alberta Taxpayers' Federation, she lays down what other people say in two and a half hours in about 10 minutes. And I'm like, uh, well,

I don't have much more because you've just explained it so concisely. And it's funny, you, you've hammered out an entire book in about an hour. And I'm like, uh, it has been a lot. It has been a lot.

And stuff that I, I guess I, I don't know. I guess I probably never paid attention to or didn't know what to look for or what have you. So I'm really, uh, appreciated of,

of, uh, Sheldon for bringing us together so that I could have you on and talk about it. But I don't want to, I got nothing but time. So if there's other things you want to make sure that people know, please don't let me end it short. Well, I guess one of the things I do is in the book I tried to put things into a context. So I kind of take us on a journey of what was going on during this time.

And so I do look at what was going on politically and what the politicians were saying and laying that out because one of the things that is remarkable is when you look at the, I'll call it the narrative and the science behind it, they've had to shift the endpoints, right? They had to shift it from, oh, we'll stop people from catching it. Oh, no, it will stop people from severe illness and death. Oh, no, you know. So there's, there was been a shifting of the science as the science became available, even though the actual science was pretty clear in this regard initially. Like it did the science shifts when there's a lot of manipulation. Okay.

I mean, science become, your, your conclusions can become more accurate as time goes on, but you don't get this shifting all over the place. That is just, they've had to shift the narrative. because the data was so bad. But it kind of goes through how science seemed, the science seemed to shift. But the political arc was always the same. When you look at what was said in the beginning, middle end,

it was very consistent. They wanted to put, when it came to the vaccines, they wanted to put as many jobs in people's arms as they could. And when you look at what they were saying and what they were doing at the time, it's very consistent. So I do bring the reader through what was going on politically, what was being said in the media, what the science was saying.

And you can, it just, to me, makes things very, very clear. It lays it out. It talks a little bit also about when you get into the safeguard failures, we'll talk about what was going on in terms of the federal vaccine mandates and what people were going through. that's all in there. Once again, for the listener, you know, as I'm listening to talk, what I'll do is in the show notes, I'll put a link directly to your book. That way, if anyone's like, ooh, I have got

to pick this up, they can just scroll down in the podcast notes, and boom, it's right there, and they can just click on it, and then away you go. Because, you know, of level of importance when it comes to understanding what took place, honestly, you've laid it out, and, in such a brief period of time, and I think it would be really important that people support you and pick up a copy of the book, Gina. Like, it's, you know, there's no level, it doesn't seem to matter how many times I think I've hit the bottom

or Doug, you know, like of how bad it is. There's always a new level, and you've just added a new level of how bad this really and truly is. Right. I think I look at the book a little bit like a reality check. Oh, you know, there's been a lot of gaslighting over the last few years. And that does a lot of bad things to people psychologically. And then when you read the book and it lays it out,

I think a lot of people will feel, you know, validated because they've been dismissed and they've been told, no, that didn't happen or it wasn't that bad and that type of thing. And when you look at it and you even look at it from the, the big picture perspective, it's like, yeah, it was bad and it did happen. And I think, I'm hoping it does bring, you know, comfort to a lot of people. But again, like, I think this builds a solid, solid case to bring in protections. Well, I appreciate you coming on. And what we're going to do here, folks, is we're, I'm just kind of making an adjustment as we go. What we're going to do is we're going to keep Gina for another 10 minutes,

but what we're going to do is we're going to slide over to substack. So we're going to end this portion, and if you want to hop over to substack, it's free. All you've got to do is just click on the link in the show notes, and we're going to ask her one or two more questions, and then we're going to let her out of here. So thanks, Gina, for hopping on, and stick with us, folks,

because we're going to hop over to substack, and we hope to see you there.